In this important new Handbook, the editors have gathered together a range of leading contributors to introduce the theory and practice of multilevel modeling.
The Handbook establishes the connections in multilevel modeling, bringing together leading experts from around the world to provide a roadmap for applied researchers linking theory and practice, as well as a unique arsenal of state-of-the-art tools. It forges vital connections that cross traditional disciplinary divides and introduces best practice in the field.
Part I establishes the framework for estimation and inference, including chapters dedicated to notation, model selection, fixed and random effects, and causal inference; Part II develops variations and extensions, such as nonlinear, semiparametric and latent class models; Part III includes discussion of missing data and robust methods, assessment of fit and software; Part IV consists of exemplary modeling and data analyses written by methodologists working in specific disciplines.
Combining practical pieces with overviews of the field, this Handbook is essential reading for any student or researcher looking to apply multilevel techniques in their own research.
Chapter 7: Model Selection for Multilevel Models
Model Selection for Multilevel Models
One of the more challenging aspects of modern applied problems is specifying the statistical model in a principled, rigorous, and ultimately defensible manner. For standard statistical analyses, such as multiple linear or logistic regression, one can rely on an established philosophy and practices to select models. Although there are various approaches to basic model specification and selection, the principles and differences amongst methods are well understood and can be easily explained, even to non-statisticians. However, only some of the fundamental principles from simpler models can be readily transported to the context of multilevel modeling, and the usual criteria and strategies for model selection must be adapted.
The purpose of this chapter is to provide ...